ANALISIS METODE K-MEANS PADA PENGELOMPOKAN PERGURUAN TINGGI MENURUT PROVINSI BERDASARKAN FASILITAS YANG DIMILIKI DESA
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Higher education is an education level that includes diplomat, undergraduate and doctoral programs. The purpose of higher education is to improve the quality of the workforce, to help improve the quality of the workforce each university must have the facilities needed in teaching and learning activities. This study discusses the Analysis of the K-Means Method in the Grouping of Universities by Province Based on the Facilities of the Village. Sources of data obtained from data collected based on documents from 2003 to 2018 through the website of the Indonesian Statistics Agency. Data is processed into 2 clusters, namely the highest facility level cluster (C1) and the lowest facility level cluster (C2). So that obtained from 34 provinces 3 provinces are grouped in high facility level clusters (C1) and 31 provinces are grouped in low facility level clusters (C2). This can be input to the government for provinces that have higher education institutions that still have inadequate facilities in each village and are of more concern to the government based on the cluster that is being conducted.Keywords: K-Means, Higher education, Grouping, Facilities
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it